Purpose The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage. Design/methodology/approach This paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives. Findings In the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions. Originality/value The analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.

PurposeThe purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search model for Dunhuang murals is proposed to help users acquire rich knowledge and services conveniently.Design/methodology/approachFirst, local and global features of images are extracted, and the visual dictionary is generated by the k-means clustering. Second, the mobile visual search model based on the bag-of-words (BOW) and multiple semantic associations is constructed. Third, the mobile visual search service system of the smart library is designed in the cloud environment. Furthermore, Dunhuang mural images are collected to verify this model.FindingsThe findings reveal that the BOW_SIFT_HSV_MSA model has better search performance for Dunhuang mural images when the scale-invariant feature transform (SIFT) and the hue, saturation and value (HSV) are used to extract local and global features of the images. Compared with different methods, this model is the most effective way to search images with the semantic association in the topic, time and space dimensions.Research limitations/implicationsDunhuang mural image set is a part of the vast resources stored in the smart library, and the fine-grained semantic labels could be applied to meet diverse search needs.Originality/valueThe mobile visual search service system is constructed to provide users with Dunhuang cultural services in the smart library. A novel mobile visual search model based on BOW and multiple semantic associations is proposed. This study can also provide references for the protection and utilization of other cultural heritages.

PurposeThis paper aims to investigate how online consumer reviews (OCRs), countdowns and self-control affect consumers' online impulse buying behavior in online group buying (OGB) and uncover the relationship between these factors.Design/methodology/approachBased on the stimulus-organism-response (SOR) framework, this research examines the effects of OCRs, countdowns and self-control on users' impulse purchases. First, the influence of emotions on impulse purchases in group purchasing is investigated. In addition, this study innovatively applies stress-coping theory to group buying research, with countdowns exerting temporal pressure on consumers and OCRs viewed as social pressure, to investigate in depth how countdowns and OCRs affect users' impulse purchase behavior. Finally, this study also surveys the moderating role of users' self-control in the impulse purchase process.FindingsThe results show that the perceived value of OCRs and positive emotions (PE) were positively correlated with impulsiveness (IMP) and the urge to buy impulsively (UBI), while negative emotions (NE) were negatively correlated with IMP. Countdowns (CD) had a positive effect on UBI. Self-control can indirectly affect users' impulse buying by negatively moderating the relationship between PE and UBI, PE and IMP and CD and UBI.Originality/valueThe research results can help group buying platforms and related participants understand the factors influencing users' impulse purchases in OGB and facilitate them to better design strategies to increase product sales.

PurposeTwitter fake accounts refer to bot accounts created by third-party organizations to influence public opinion, commercial propaganda or impersonate others. The effective identification of bot accounts is conducive to accurately judge the disseminated information for the public. However, in actual fake account identification, it is expensive and inefficient to manually label Twitter accounts, and the labeled data are usually unbalanced in classes. To this end, the authors propose a novel framework to solve these problems.Design/methodology/approachIn the proposed framework, the authors introduce the concept of semi-supervised self-training learning and apply it to the real Twitter account data set from Kaggle. Specifically, the authors first train the classifier in the initial small amount of labeled account data, then use the trained classifier to automatically label large-scale unlabeled account data. Next, iteratively select high confidence instances from unlabeled data to expand the labeled data. Finally, an expanded Twitter account training set is obtained. It is worth mentioning that the resampling technique is integrated into the self-training process, and the data class is balanced at the initial stage of the self-training iteration.FindingsThe proposed framework effectively improves labeling efficiency and reduces the influence of class imbalance. It shows excellent identification results on 6 different base classifiers, especially for the initial small-scale labeled Twitter accounts.Originality/valueThis paper provides novel insights in identifying Twitter fake accounts. First, the authors take the lead in introducing a self-training method to automatically label Twitter accounts from the semi-supervised background. Second, the resampling technique is integrated into the self-training process to effectively reduce the influence of class imbalance on the identification effect.

Purpose Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images. Design/methodology/approach The convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results. Findings The results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved. Originality/value This study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.